Evidence map›Paper›PMID 30108547›Full record

ReviewFrontiers in endocrinology2018

Biomarkers for Macrosomia Prediction in Pregnancies Affected by Diabetes.

Sofia Nahavandi, Jas-Mine Seah, Alexis Shub, Christine Houlihan, Elif I Ekinci

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in endocrinology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
2.8field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 57 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Sofia NahavandiDepartment of Medicine, The University of Melbourne, Melbourne, VIC, Australia.
Jas-Mine SeahDepartment of Endocrinology, Austin Health, Melbourne, VIC, Australia.
Alexis ShubDepartment of Medicine, The University of Melbourne, Melbourne, VIC, Australia.
Christine HoulihanDepartment of Medicine, The University of Melbourne, Melbourne, VIC, Australia.
Elif I EkinciDepartment of Medicine, The University of Melbourne, Melbourne, VIC, Australia.
Mercy Hospital for Women · AUUniversity of Melbourne · AUAustin Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large birthweight, or macrosomia, is one of the commonest complications for pregnancies affected by diabetes. As macrosomia is associated with an increased risk of a number of adverse outcomes for both the mother and offspring, accurate antenatal prediction of fetal macrosomia could be beneficial in guiding appropriate models of care and interventions that may avoid or reduce these associated risks. However, current prediction strategies which include physical examination and ultrasound assessment, are imprecise. Biomarkers are proving useful in various specialties and may offer a new avenue for improved prediction of macrosomia. Prime biomarker candidates in pregnancies with diabetes include maternal glycaemic markers (glucose, 1,5-anhydroglucitol, glycosylated hemoglobin) and hormones proposed implicated in placental nutrient transfer (adiponectin and insulin-like growth factor-1). There is some support for an association of these biomarkers with birthweight and/or macrosomia, although current evidence in this emerging field is still limited. Thus, although biomarkers hold promise, further investigation is needed to elucidate the potential clinical utility of biomarkers for macrosomia prediction for pregnancies affected by diabetes.

Indexed as

biomarkersbirthweightdiabetesmacrosomiapregnancy

Identifiers

PMID30108547
PMCPMC6079223
OpenAlexW2885595798

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.